Make business rules and expert judgment
usable by agents—and reviewable by people.
We structure concepts, relationships, business rules, exceptions, and supporting evidence from policies, manuals, data definitions, business documents, and expert knowledge. We organize the results in an Ontology and Knowledge Graph and apply validation, approval, access, versioning, and change controls. Approved knowledge can then be made available to agents and applications through services and APIs.
Finding a document is not the same as applying a business rule
RAG is useful for retrieving relevant material and supporting answer generation. Applying knowledge to analysis and decisions also requires explicit definitions, rule conditions, order of application, thresholds, exceptions, effective periods, approval status, and procedures for handling conflicting evidence.
Meaning & Context
Define business concepts, attributes, relationships, states, and events so terms can be interpreted in their business context.
Rules & Exceptions
Represent calculation criteria, decision conditions, thresholds, the order in which rules apply, exceptions, and approval procedures explicitly.
Evidence & Conflict
Record source references and effective periods, and distinguish conflicting assertions so their evidence and conditions can be compared.
Ownership & Change
Manage owners, approval status, versioning, access, and change impact to define which knowledge is approved for use.
Build the knowledge model, rules, services, and controls as one system
Business Ontology
Define the core business concepts, attributes, relationships, states, events, responsibilities, and decision context.
Knowledge Graph
Represent documents, data, policies, organizations, business objects, and supporting evidence as identifiable nodes and relationships.
Knowledge Extraction & Linking
Extract knowledge candidates, resolve the same entities across different names and codes, map relationships, and handle duplicates and conflicts.
Decision Knowledge & Business Rules
Structure business questions, metrics, decision conditions, thresholds, exceptions, approvals, and execution conditions.
Knowledge Services & APIs
Expose governed knowledge so agents and applications can query, search, and traverse relationships and use the relevant rules and evidence.
Knowledge Governance
Define governance procedures for source tracking, ownership, quality status, versioning, permissions, approval, change, and retirement.
Make identity, conflict, approval, and change rules explicit
A graph of entities and relationships is not enough for operational use. The knowledge model also needs explicit rules for deciding what represents the same entity, which relationships are permitted, and how conflicts, approval, and change are handled.
Identity & Meaning Rules
Define unique identifiers, names, attributes, business meaning, and the scope in which each entity and relationship applies.
Merge & Conflict Rules
Define when duplicate entities can be merged and how conflicting assertions, exceptions, and effective periods are handled.
Constraint & Quality Rules
Apply rules for required attributes, permitted relationships, value ranges, completeness, and consistency.
Provenance & Approval Rules
Track sources, supporting evidence, owners, validity periods, and approval status, and define which knowledge is approved for use.
Access & Change Rules
Define access rules for people and agents, together with versioning, change, retirement, and impact-review procedures.
Business-Question Validation
Use representative business questions to check whether the model provides the required knowledge and evidence and to identify gaps and unresolved areas.
Use LLMs to prepare structured candidates, then validate them against sources and rules
LLMs can extract candidate concepts, relationships, and business rules from documents and data. Schemas guide the extraction; outputs from multiple models can be compared; and each candidate is checked against source documents, data definitions, rules, and representative business questions. Designated reviewers separate approved knowledge from unresolved items before publication.
Source Analysis & Extraction
Identify candidate concepts, relationships, rules, and source references in policies, manuals, data dictionaries, SQL, reports, and interview notes.
Schema-Guided Ontology
Define entity types, attributes, permitted relationships, and output structures to control what is extracted and how it is represented.
Entity Resolution & Linking
Match the same entity across different names and codes, then link documents, data, business objects, and supporting evidence.
Evidence & Multi-Model Review
Compare outputs from multiple LLMs and verify candidates against source documents and data definitions to identify omissions, unsupported inferences, and conflicts.
Rule & Quality Validation
Check identifiers, required attributes, permitted relationships, and business rules, and use representative questions to validate the structure and content.
Approval, Versioning & Change
Separate approved knowledge from unresolved items and manage approval status, versioning, change and retirement history, and impact on agents and applications.
Separate the knowledge model from the technology used to implement it
The ontology, business rules, provenance, and validation criteria are managed independently of the products used to implement them. Storage, search, relationship traversal, and service components are selected according to data volume, query patterns, security requirements, and the operating environment.
Knowledge Model
Define ontology schemas, entities and relationships, business rules, and the provenance model independently of the implementation technology.
Build & Validation Pipeline
Design an automation-ready pipeline for source collection, LLM extraction, entity resolution, rule and evidence validation, and publication of approved knowledge to the knowledge store.
Knowledge Store
Select tables, relational stores, graph databases, and search indexes according to the data structure, relationship-traversal needs, and performance requirements.
Search & Reasoning Services
Combine keyword, vector, and graph search with query services and APIs so RAG systems and agents can access the required knowledge, relationships, rules, and evidence.
Security & Governance
Preserve source-data access controls and implement controls for permissions, provenance, approval status, usage history, and change tracking in the knowledge layer.
Deployment & Operations
Configure the architecture for Snowflake, cloud, on-premises, or hybrid environments and define release and change boundaries for models, schemas, pipelines, and services.
Put governed knowledge into search, analysis, workflows, and change-impact review
Knowledge Search & RAG
Provide relevant documents, concepts, and evidence together with source references and the scope in which the knowledge applies.
Agentic Analytics
Provide the metrics, analytical steps, decision conditions, exceptions, and evidence an agent uses to prepare analysis for review.
Decision Support & Workflow
Use business conditions and approval rules to identify the next items for review and prepare alternatives for human approval.
Impact & Change Analysis
Review related concepts, rules, agents, and applications when policies or data definitions change.
Start with the workflow, questions, and decisions the knowledge must support
Tell us about the target workflow, available documents and data, recurring questions, current decision criteria, and the agents or applications that will use the knowledge. We will help define the initial scope for the Ontology and Knowledge Graph.
Discuss Enterprise Knowledge Engineering →